A mystery player causing a stir in the world of the complex strategy game Go has been revealed as an updated version of AlphaGo. Known only by the name ‘Master(P)’, since late December the anonymous player has beaten the world’s best at Go in a string of online games, including defeating current world number one, 19-year-old Ke Jie.

Starting January 11, four of the world’s best professional poker players will compete against artificial intelligence developed by CMU to determine whether a computer can beat humans playing one of the world’s toughest poker games.

A directed acyclic computational graph builder, built from scratch on numpy and C with auto-differentiation support. A clean code base and a great way to learn how modern frameworks for Deep Learning are built and used. Demos include LeNet, LSTM Question Answering, Neural Turing Machines, and DQN.

Poker is the quintessential game of imperfect information, and it has been a longstanding challenge problem in artificial intelligence. This paper introduces DeepStack, a new algorithm for imperfect information settings such as poker. In a study involving dozens of participants and 44,000 hands of poker, DeepStack becomes the first computer program to beat professional poker players in heads-up no-limit Texas hold'em.

A methodology to analyze and interpret decisions from a neural model by observing the effects on the model of erasing various parts of the representation, such as input word-vector dimensions, intermediate hidden units, or input words. (Stanford)

This report summarizes the tutorial presented by the author at NIPS 2016 on generative adversarial networks (GANs). The tutorial describes: (1) Why generative modeling is a topic worth studying, (2) how generative models work, and how GANs compare to other generative models, (3) the details of how GANs work, (4) research frontiers in GANs, and (5) state-of-the-art image models that combine GANs with other methods. (Ian Goodfellow)